Imaging systems which automate assessment are known. Medical image processing systems process images to derive certain diagnostic information, for example, from x-ray, magnetic resonance images (MRI) and tomosynthesis. In particular these are used to help in the diagnosis of cancers and measurement of object composition.
Often image processing systems receive a number of images, usually closely related, for example, images of the same subject with slight variation in aspect or timing. Such multiplicity and variety can infer more information than a single image. Further, selective information from multiple images can enhance reliability, for example, exposing a hitherto obstructed object. Thus availability of comparative images can help direct and verify image processing. However, particularly in the field of radiography, images or features of an image are often difficult to interpret due to errors or unknown values in the imaging physics data.
It is known to use mammography to image breast tissue. A mammogram is created by sending x-ray photons towards the breast and detecting how many x-ray photons pass through. The smaller the number of x-ray photons that pass through, the denser the breast tissue. To quantify the image based on an ‘absolute’ model of the physics along with assumed properties of the breast requires that all the imaging physics data be known and accurate (for example, photon flux, X-ray tube voltage, pixel area and time of exposure). Thus the need to use in-image reference values.
Research undertaken by Highnam and Brady (Highnam and Brady, “Mammographic Image Analysis, Kluwer Academic Publishers 1999) resulted in an understanding of how to automatically compute the density of breast tissue from a mammogram and thereby quantify interesting tissue. Interesting tissue may comprise, for example, fibrous tissue, glandular tissue, water, or cancerous tissue.
The method of Highnam and Brady uses a combination of image processing and x-ray physics and associated imaging physics data which generally relates to conversion of a pixel value P measured at coordinates (x,y), P(x,y), in the mammogram into a thickness of fat, hfat(x,y) cm, and a thickness of ‘interesting tissue’, hint(x,y) cm, where ‘interesting tissue’ could be fibrous tissue, glandular tissue, water, or cancerous tissue.
‘Interesting tissue’ may comprise and/or hide cancers. Thus, irrespective of the imaging technique used, accurate segmentation of the breast in mammograms is essential for effective location and diagnosis of cancers. Segmentation guides the search for abnormalities to the relevant region and enables comparable analysis for example temporal analysis or automated comparison of corresponding images.
PCT/GB2010/001472 provides means to automatically estimate breast composition by calculating hint and hfat values; summing up the hint and hfat values; and computing breast density. The method resolves erroneous indications and error in calculation and calculation bases by always finding a reliable reference spot in an image which then allows calculation of an explicit calibration error. The method is consequently robust to errors and unknown data in the imaging data, and has an associated calibration error factor which can be used to alert the user as necessary.
Further, accurate imaging physics data is unnecessary and in fact the milliampere-second (mAs) and much of the detector information can be ignored, other than assuming that the detector is linear with a known offset.
Such a method works well, but a key step is to define an inner breast edge in which to identify a reference spot—effectively an internal reference point for each image.
In digital breast tomosynthesis (DBT) multiple low-dose x-ray projections of an object are taken and reconstructed to create a pseudo 3D view of the object. The central projection of the DBT is effectively a low dose mammogram and so PCT/GB2010/001472 applies.
Despite its diagnostic advantages, DBT presents challenges for clinical workflow as it involves increased time for reading and interpretation and thereby increased cost and potential for reader oversight, even with the aid of CAD.